In PLOS Genetics, researchers from New Zealand and the US looks at the gene expression consequences of genome structure in Epichloë festucae, a filamentous fungus that serves as a symbiont for cool season grasses. After establishing a gapless genome for E. festucae with a combination of short Illumina and long Pacific Biosciences read sequencing, the team used Hi-C mapping and RNA sequencing to investigate genome structure features contributing to symbiosis-related transcriptional patterns in the fungus. "Our results reveal a genome in which very repeat-rich blocks of DNA with discrete boundaries are interspersed by gene-rich sequences that are almost repeat-free," the authors write, noting that "the three-dimensional structure of the genome is anchored by these repeat blocks, which act to isolate transcription in neighboring gene-rich regions."
A Vanderbilt University-led team describes a statistical framework called sc-UniFrac for quantifying cell population diversity based on single-cell RNA sequencing data for a paper in PLOS Biology. The sc-UniFrac approach "allows cells that drive differences between samples to be easily identified," the researchers explain. Based on their findings in simulated and real single-cell RNA sequencing datasets, the authors suggest that sc-UniFrac can be used for "assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools."
For a paper appearing in PLOS One, researchers in Brazil and Guiana consider the chemical and population genetic diversity in Uncaria guianensis, a native Amazonian plant with purported anti-inflammatory properties. The team used high performance liquid chromatography and "sequence-related amplified polymorphism" analyses to assess leaf concentrations of two previously proposed chemical markers for the plant and to profile genetic variation, respectively, in individuals from eight U. guianensis populations in Brazil. With this approach, the authors identified three genetic clusters, but found only a few plants with significant levels of the alkaloid compounds considered.